扫雷器发射架电液伺服系统改进自抗扰控制

针对某爆破扫雷器发射架电液位置伺服系统响应速度慢、跟踪精度低、抗扰能力差的问题，设计一种改进自抗扰控制器。利用BP神经网络强大的自学习和非线性逼近能力对自抗扰控制器中的关键参数在线整定，并结合遗传算法(genetic algorithm，GA)对网络的初始权值进行优化；利用AMEsim和Simulink软件对该改进自抗扰控制器进行联合仿真验证。结果表明：该控制方法可有效提高系统的抗干扰能力，同时保证扫雷器的调炮速度和精度。

Improved Active Disturbance Rejection Control for Electro-hydraulic Servo System of Mine Sweeper Launcher

An improved active disturbance rejection controller (ADRC) was designed to solve the problems of slow response, low tracking accuracy and poor anti-disturbance ability of the electro-hydraulic position servo system of a mine sweeper launcher. BP neural network, which has powerful self-learning and nonlinear approximation ability, is used to adjust the key parameters of ADRC online, and combined with genetic algorithm (GA).The initial weights of the network are optimized, and AMEsim and Simulink software are used to co-simulate and verify the improved ADRC. The results show that the control method can effectively improve the anti-interference ability of the system, and ensure the speed and accuracy of gun adjustment of the mine sweeper.